Project Summary: Bacterial cells have a repertoire of responses that can be used to survive under different types of environmental stress. Changes in carbon sources cause cells to turn on specific metabolic genes, which are later repressed when those sources are depleted. Antibiotic exposure can trigger the expression of molecular pumps that remove the antibiotic from the cell, or the production of enzymes that specifically degrade it. In a continually fluctuating environment, the process of turning genes on and off can be costly, especially under antibiotic exposures when cells are rapidly killed if the response gene is off. Our work shows that bacteria combine their responses with molecular memory mechanisms that allow cells to avoid the costs of frequent gene regulation in a fluctuating environment. The project will determine the conditions under which molecular memory is a beneficial strategy, and by using a combination of synthetic biology, microfluidics, microscopy, and modeling, we will experimentally perturb and measure the costs and benefits of memory. We will construct bacterial strains with a range of memory levels, and perform competition experiments to determine how cellular memory profiles are tuned to the external environment. The proposed experiments make use of a custom-built microfluidic ?chemoflux? system that we developed, in which bacterial populations grow in monolayers, tracked at single cell resolution under the microscope, while the growth media can be arbitrarily fluctuated in time. Using the chemoflux and our image analysis algorithms, we are able to quantify tens of thousands of cells over hundreds of generations, and thereby measure population dynamics in fluctuating environments at a resolution that was previously unattainable. We will use two different levels of modeling, including a coarse-grained approach in which timescales and rate constants are the main parameters, and the goal is to predict the optimal amount of memory for a given response and fluctuating environment; and a detailed, single cell stochastic model, in which the process of cellular elongation and division is precisely quantified and modeled under changing conditions. These two representations will address different aspects of memory, and allow us to bridge from detailed laboratory measurements to the general biological principles that underlie bacterial survival.